class_docstr: Wrapper class to load the vocoder trained with parallel_wavegan repo.
docstr: Generate waveform with pretrained vocoder.
  
  Args:
      feats (Tensor): Feature tensor (T_feats, #mels).
  
  Returns:
      Tensor: Generated waveform tensor (T_wav).
docstr_tok: ["Generate","waveform","with","pretrained","vocoder",".","Args",":","feats","(","Tensor",")",":","Feature","tensor","(","T_feats",",","#","mels",")",".","Returns",":","Tensor",":","Generated","waveform","tensor","(","T_wav",")","."]
code: def forward(self, feats: torch.Tensor) -> torch.Tensor:
      """Generate waveform with pretrained vocoder.
  
      Args:
          feats (Tensor): Feature tensor (T_feats, #mels).
  
      Returns:
          Tensor: Generated waveform tensor (T_wav).
  
      """
      return self.vocoder.inference(
          feats,
          normalize_before=self.normalize_before,
      ).view(-1)
  
file_path: espnet/espnet2/tts/utils/parallel_wavegan_pretrained_vocoder.py
imports: import logging
  import os
  from pathlib import Path
  from typing import Optional, Union
  import torch
  import yaml
label_desc: Use image node_id 2 for calling the ParallelWaveGANPretrainedVocoder obj's underlying member method code with example usage: obj.forward(feats) and returns: self